Visual Media Evaluator

AI Trainer Jobs; United States; Remote; USD 28,000 - 55,000; Part time

Benefits offered by this job

Remote work

Job summary

AI Trainer Jobs is seeking a Visual Media Evaluator to work remotely on AuraOne's quality rubric. You will review prompts and responses, compare outputs, label edge cases, and write structured feedback for retraining the model.

The role is a remote contractor with US-eligible placement; compensation is hourly and confirmed after the interview process. You will assess visual media outputs, judge spatial reasoning, and contribute to calibration and rubric updates.

Qualifications

• Experience with annotation or data labeling workflows.
• Ability to apply rubrics consistently across long batches.
• Clear written reasoning naming the issue and rubric clause.
• Strong attention to detail and reliability in async work.

Responsibilities

• Evaluate visual media outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, policy violations, and unsafe content with structured tags.
• Capture ambiguous prompts and report for rubric updates.
• Maintain reviewer quality scores through calibration against gold standards.

Job description

Visual Media Evaluator is a remote evaluation track for reviewing visual media evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Category: Video, Vision & Spatial AI · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Visual Media Evaluator is a remote evaluation track for reviewing visual media evaluation prompts and responses against AuraOne's quality rubric.

Role details

About the role

Visual Media Evaluator is a remote evaluation track for reviewing visual media evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn visual media evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.

Responsibilities

• Evaluate visual media evaluation model outputs against a versioned rubric and assign severity tags for Visual Media Evaluator assignments.
• Compare paired responses and pick the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

What you should bring

• Prior evaluation, annotation, or human-rater experience on visual media evaluation or adjacent content for Visual Media Evaluator work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Reliable async availability for at least 10 hours per week.

Example tasks

• Compare two visual media evaluation model responses to the same prompt and pick the stronger one with rationale.
• Tag an unsafe response with the correct policy category and severity.
• Audit a 50-row batch for rubric consistency and report drift to the program lead.
• Propose a rubric clarification after spotting a recurring failure mode.

Useful experience

• Background in linguistics, content moderation, or trust & safety review.
• Experience with inter-rater agreement metrics and calibration cycles.
• Domain expertise that lets you spot subject-matter errors automated checks miss.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Visual Media evaluation
• Design
• AI model evaluation